Multi-channel Fourier packet transform of EEG: optimal representation and time-varying coherence

Multi-channel recording of electroencephalogram (EEG) provides a measure of spatial-temporal pattern of cognitive processes. When oscillatory activities are going to be studied, the time-domain EEG signal can be analyzed via Fourier or wavelet transform. However the loss of temporal information after Fourier transform and the unavailability of phase information in wavelet transform limit their applicability in EEG analysis. In this paper, multi-channel Fourier packet transform is introduced. The algorithm resembles the wavelet packet transform by its binary tree search for an optimal selection of orthogonal basis, but extends the application to the multi-channel scenario. It aims to provide a sparse signal representation to localize features in the spatial-spectral-temporal domain. Since the decomposed atoms are spatially coherent components, analysis of time-varying synchrony across scalp locations is then possible.published_or_final_versio

The investigation of time-varying synchrony of EEG during sentence learning using wavelet analysis

The synchrony analysis has been used as a tool for the purpose of investigating how the cognitive processes take place between different brain regions when the specified learning task is going on. We propose a novel method based on the time-frequency representation for quantifying synchronization between two channel EEG with both temporal and spectral resolution. The presented method employed the wavelet transform for cross coherent spectral analysis of the EEG signals recorded during sentences recognizing and learning. The wavelet-coherent magnitude spectra provide the information indicating the degree of coherence and the cross-wavelet phase relation serves to indicate the direction of information flow between two EEG channels on different cortical regions. Real EEG recordings are collected based on a cognitive target. It is observed from both the magnitude spectra and phase of the wavelet coherence that there are obvious differences between the identification of both Chinese and English sentences. These are helpful for the research on the English study for Chinese students.published_or_final_versio

Analysis of time-varying synchronization of EEG during sentences identification

The study of the synchronization of EEG signals can help us to understand the underlying cognitive processes and detect the learning deficiencies since the oscillatory states in the EEG reveal the rhythmic synchronous activity in large networks of neurons. As the changes of the physiological states and the relative environment exist when cognitive and information processing take place in different brain regions at different times, the practical EEGs therefore turn out to be extremely non-stationary processes. To investigate how these distributed brain regions are linked together and the information is exchanged with time, this paper proposes a modern time-frequency coherent analysis method that employs an alternative way for quantifying synchronization with both temporal and spatial resolution. Wavelet coherent spectrum is defined such that the degree of synchronization and information flow between different brain regions can be described. Several real EEG data are analysed under the cognitive tasks of sentences identification in both English and Chinese. The time-varying synchronization between the brain regions involved in the processing of sentences exhibited that a common neural network is activated by both English and Chinese sentences. The results of the presented method are helpful for studying English and Chinese learning for Chinese students.published_or_final_versio

A method for identifying non-Gaussian parametric model with time-varying coefficients

A method for identifying a non-Gaussian AR model with time-varying parameters is addressed. The proposed approach is based on the application of higher-order spectra (HOS) and wavelet analysis. To solve the problem and identify the characteristics of the time-varying linear system, a time-varying parametric model is proposed as a non-Gaussian AR model. The model coefficients that characterize the time-varying system are the functions of time and can be represented by a family of wavelet basis functions, having the invariant basis coefficients. This method can well track the changes of the model coefficients. The experimental results show the effectiveness of the proposed approach.published_or_final_versio

An approach for identification of non-Gaussian linear system with time-varying parameters

A new approach for identification of non-Gaussian linear system with time-varying parameters is addressed in this paper. The proposed method is based on the application of higher-order spectra (HOS) and wavelet analysis. In order to solve the problem and identify the characteristics of the time-varying linear system, a time-varying parametric model is proposed as non-Gaussian AR model. The model parameters that characterize the time-varying system are functions of time and can be represented by a family of wavelet basis functions, of which the corresponding basis coefficients are invariant. This method can well track the changes of the model parameters, and the results show its effectiveness of the proposed approach.published_or_final_versio

Second order statistics based blind source separation for artifact correction of short ERP epochs

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.published_or_final_versio

How Does Experience Modulate Auditory Spatial Processing in Individuals with Blindness?

published_or_final_versio

Effects of cold water immersion on muscle oxygenation during repeated bouts of fatiguing exercise : a randomized controlled study

2015-2016 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

An analysis of logistics response times for requisitions of naval aviation repairable items

Minimized safety level investment, while achieving high service levels and low customer wait time, is critical to the performance of the United States Navy supply system. The Naval Inventory Control Point (NAVICP) uses the Uniform Inventory Control Program to compute safety levels for each of the stock items they maintain. To assist in computing these levels, NAVICP aggregates repairable items based on demand and cost. The performance metrics used to measure the effectiveness of the model, Supply Material Availability and Average Days Delay, are affected by this aggregation. The purpose of the thesis is to describe an alternative methodology of aggregation that will allow NAVICP to allocate its item management skills more efficiently. The proposed methodology, based on item cost, demand, and Logistics Response Times for requisitions, can improve inventory performance without increasing the workload of item managers. Using analysis of variance, an analytical approach is adopted to ascertain whether an item has an average Logistics Response Time that exceeds the Navy's goal. It is shown that the proposed aggregation can improve Supply Material Availability and safety level investments while better managing items based on Logistics Response Time.http://archive.org/details/annalysisoflogis109455961LCDR, SC, USNApproved for public release; distribution is unlimited